!pip install -U finance-datareader

import FinanceDataReader as fdr

df_krx=fdr.StockListing('krx')
df_krx

df_krx['Name'].unique()

    array(['삼성전자', 'LG에너지솔루션', 'SK하이닉스', ..., '앙츠', '명진홀딩스', '베른'],
          dtype=object)

df_krx.columns

    Index(['Code', 'ISU_CD', 'Name', 'Market', 'Dept', 'Close', 'ChangeCode',
           'Changes', 'ChagesRatio', 'Open', 'High', 'Low', 'Volume', 'Amount',
           'Marcap', 'Stocks', 'MarketId'],
          dtype='object')

import pandas as pd
data=pd.DataFrame({'Code':[],'Name':[],'Market':[],'Amount':[],'Stocks':[]})
j=0
for i in df_krx['Name'].unique():
  if '삼성' in i :
    data=pd.concat([data,df_krx[df_krx['Name']==i][['Code','Name','Market','Amount','Stocks']]],ignore_index=True)
    j+=1
data=data[:10]
data=data.sort_values(by='Stocks',ascending=False,ignore_index=True)
data.sort_values(by='Stocks',ascending=False,ignore_index=True)

dic=dict()
i=0
for i in data['Name'].unique():
  if '삼성' in i :
    for x,y in enumerate(df_krx[df_krx['Name']==i]['Code']):
      dic[i]=y
print(dic)

    {'삼성전자': '005930', '삼성전자우': '005935', '삼성생명': '032830', '삼성엔지니어링': '028050', '삼성물산': '028260', '삼성에스디에스': '018260', '삼성전기': '009150', '삼성바이오로직스': '207940', '삼성SDI': '006400', '삼성화재': '000810'}

data[['Name','Code']]

import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.gridspec as gridspec
grid=gridspec.GridSpec(2,5)#그래프를 그릴 도화지 개수 
plt.figure(figsize=(50,50))
plt.subplots_adjust(wspace=0.3,hspace=0.3) #그래프 간의 거리 조절

#서버플롯 그리기
for idx, feature in enumerate(dic.keys()):
  data=fdr.DataReader(dic[feature],'2023')
  ax=plt.subplot(grid[idx])
  sns.barplot(x=data.index,y='Change',data=data,palette='pastel',ax=ax)
  ax.set_title(f'2023 {feature} Distribution')

import pandas as pd
data=pd.DataFrame({'Code':[],'Name':[],'Market':[],'Amount':[],'Stocks':[]})
j=0
for i in df_krx['Name'].unique():
  if 'LG' in i :
    data=pd.concat([data,df_krx[df_krx['Name']==i][['Code','Name','Market','Amount','Stocks']]],ignore_index=True)
    j+=1
data=data[:10]
data=data.sort_values(by='Stocks',ascending=False,ignore_index=True)
data.sort_values(by='Stocks',ascending=False,ignore_index=True)

dic=dict()
i=0
for i in data['Name'].unique():
  if 'LG' in i :
    for x,y in enumerate(data[data['Name']==i]['Code']):
      dic[i]=y
print(dic)

    {'LG유플러스': '032640', 'LG디스플레이': '034220', 'LG에너지솔루션': '373220', 'LG전자': '066570', 'LG': '003550', 'LG화학': '051910', 'LG이노텍': '011070', 'LG전자우': '066575', 'LG생활건강': '051900', 'LG화학우': '051915'}

data[['Name','Code']]

import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.gridspec as gridspec
grid=gridspec.GridSpec(2,5)#그래프를 그릴 도화지 개수 
plt.figure(figsize=(50,50))
plt.subplots_adjust(wspace=0.3,hspace=0.3) #그래프 간의 거리 조절

#서버플롯 그리기
for idx, feature in enumerate(dic.keys()):
  data=fdr.DataReader(dic[feature],'2023')
  ax=plt.subplot(grid[idx])
  sns.barplot(x=data.index,y='Change',data=data,palette='pastel',ax=ax)
  ax.set_title(f'2023 {feature} Distribution')

import pandas as pd
data=pd.DataFrame({'Code':[],'Name':[],'Market':[],'Amount':[],'Stocks':[]})
j=0
for i in df_krx['Name'].unique():
  if 'SK' in i :
    data=pd.concat([data,df_krx[df_krx['Name']==i][['Code','Name','Market','Amount','Stocks']]],ignore_index=True)
    j+=1
data=data[:10]
data=data.sort_values(by='Stocks',ascending=False,ignore_index=True)
data.sort_values(by='Stocks',ascending=False,ignore_index=True)

dic=dict()
i=0
for i in data['Name'].unique():
  if 'SK' in i :
    for x,y in enumerate(data[data['Name']==i]['Code']):
      dic[i]=y
print(dic)

    {'SK하이닉스': '000660', 'SK텔레콤': '017670', 'SK스퀘어': '402340', 'SK이노베이션': '096770', 'SK바이오팜': '326030', 'SK바이오사이언스': '302440', 'SK': '034730', 'SK아이이테크놀로지': '361610', 'SKC': '011790', 'SK케미칼': '285130'}

data[['Name','Code']]

import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.gridspec as gridspec
grid=gridspec.GridSpec(2,5)#그래프를 그릴 도화지 개수 
plt.figure(figsize=(50,50))
plt.subplots_adjust(wspace=0.3,hspace=0.3) #그래프 간의 거리 조절

#서버플롯 그리기
for idx, feature in enumerate(dic.keys()):
  data=fdr.DataReader(dic[feature],'2023')
  ax=plt.subplot(grid[idx])
  sns.barplot(x=data.index,y='Change',data=data,palette='pastel',ax=ax)
  ax.set_title(f'2023 {feature} Distribution')

import pandas as pd
data=pd.DataFrame({'Code':[],'Name':[],'Market':[],'Amount':[],'Stocks':[]})
j=0
for i in df_krx['Name'].unique():
  if '카카오' in i :
    data=pd.concat([data,df_krx[df_krx['Name']==i][['Code','Name','Market','Amount','Stocks']]],ignore_index=True)
    j+=1
data=data[:10]
data=data.sort_values(by='Stocks',ascending=False,ignore_index=True)
data.sort_values(by='Stocks',ascending=False,ignore_index=True)

dic=dict()
i=0
for i in data['Name'].unique():
  if '카카오' in i :
    for x,y in enumerate(data[data['Name']==i]['Code']):
      dic[i]=y
print(dic)

    {'카카오뱅크': '323410', '카카오': '035720', '카카오페이': '377300', '카카오게임즈': '293490'}

data[['Name','Code']]

import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.gridspec as gridspec
grid=gridspec.GridSpec(2,2)#그래프를 그릴 도화지 개수 
plt.figure(figsize=(10,10))
plt.subplots_adjust(wspace=0.3,hspace=0.3) #그래프 간의 거리 조절

#서버플롯 그리기
for idx, feature in enumerate(dic.keys()):
  data=fdr.DataReader(dic[feature],'2023')
  ax=plt.subplot(grid[idx])
  sns.barplot(x=data.index,y='Change',data=data,palette='pastel',ax=ax)
  ax.set_title(f'2023 {feature} Distribution')

import pandas as pd
data=pd.DataFrame({'Code':[],'Name':[],'Market':[],'Amount':[],'Stocks':[]})
j=0
for i in df_krx['Name'].unique():
  if '현대' in i :
    data=pd.concat([data,df_krx[df_krx['Name']==i][['Code','Name','Market','Amount','Stocks']]],ignore_index=True)
    j+=1
data=data[:10]
data=data.sort_values(by='Stocks',ascending=False,ignore_index=True)
data.sort_values(by='Stocks',ascending=False,ignore_index=True)

dic=dict()
i=0
for i in data['Name'].unique():
  if '현대' in i :
    for x,y in enumerate(data[data['Name']==i]['Code']):
      dic[i]=y
print(dic)

    {'현대차': '005380', '현대제철': '004020', '현대건설': '000720', '현대로템': '064350', '현대모비스': '012330', '현대중공업': '329180', 'HD현대': '267250', '현대미포조선': '010620', '현대글로비스': '086280', '현대차2우B': '005387'}

data[['Name','Code']]

import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.gridspec as gridspec
grid=gridspec.GridSpec(3,4)#그래프를 그릴 도화지 개수 
plt.figure(figsize=(30,30))
plt.subplots_adjust(wspace=0.3,hspace=0.3) #그래프 간의 거리 조절

#서버플롯 그리기
for idx, feature in enumerate(dic.keys()):
  data=fdr.DataReader(dic[feature],'2023')
  ax=plt.subplot(grid[idx])
  sns.barplot(x=data.index,y='Change',data=data,palette='pastel',ax=ax)
  ax.set_title(f'2023 {feature} Distribution')